Identification of Nonparametric Nonlinear Systems

Presently, a modelling and identification of nonlinear systems is proposed. This study is developed based on spectral approach. The proposed nonlinear system is nonparametric and can be described by Hammerstein models. These systems consist of nonlinear element followed by a linear block. This latter (the linear subsystem) is not necessarily parametric and the nonlinear function can be nonparametric smooth nonlinearity. This identification problem of Hammerstein models is studied in the presence of possibly infinite-order linear dynamics. The determination of linear and nonlinear block can be done using a unique stage

Molecular Evolution of Multiple Arylalkylamine N-Acetyltransferase (AANAT) in Fish

Arylalkylamine N-acetyltransferase (AANAT) catalyzes the transfer of an acetyl group from acetyl coenzyme A (AcCoA) to arylalkylamines, including indolethylamines and phenylethylamines. Multiple aanats are present in teleost fish as a result of whole genome and gene duplications. Fish aanat1a and aanat2 paralogs display different patterns of tissue expression and encode proteins with different substrate preference: AANAT1a is expressed in the retina, and acetylates both indolethylamines and phenylethylamines; while AANAT2 is expressed in the pineal gland, and preferentially acetylates indolethylamines. The two enzymes are therefore thought to serve different roles. Here, the molecular changes that led to their specialization were studied by investigating the structure-function relationships of AANATs in the gilthead seabream (sb, Sperus aurata). Acetylation activity of reciprocal mutated enzymes pointed to specific residues that contribute to substrate specificity of the enzymes. Inhibition tests followed by complementary analyses of the predicted three-dimensional models of the enzymes, suggested that both phenylethylamines and indolethylamines bind to the catalytic pocket of both enzymes. These results suggest that substrate selectivity of AANAT1a and AANAT2 is determined by the positioning of the substrate within the catalytic pocket, and its accessibility to catalysis. This illustrates the evolutionary process by which enzymes encoded by duplicated genes acquire different activities and play different biological roles

Spectral Determination of Nonlinear System Parameters

In this paper we propose an identification method of nonlinear system. This later can be structured by Wiener models. The determination of nonlinear system parameters can be done using spectral analysis. The system nonlinearity is allowed to be noninvertible general shape nonlinearity but it must be approximated by a polynomial function. The polynomial degree n can vary from one interval to another. The linear dynamic element is not-necessarily parametric but BIBO stable. In this work, a spectral method is developed allowing the estimates of the complex frequency gain as well as the estimates of nonlinear block parameters the identification method is built using one stage

Identification of Nonparametric Nonlinear Systems

Presently, a modelling and identification of nonlinear systems is proposed. This study is developed based on spectral approach. The proposed nonlinear system is nonparametric and can be described by Hammerstein models. These systems consist of nonlinear element followed by a linear block. This latter (the linear subsystem) is not necessarily parametric and the nonlinear function can be nonparametric smooth nonlinearity. This identification problem of Hammerstein models is studied in the presence of possibly infinite-order linear dynamics. The determination of linear and nonlinear block can be done using a unique stage